AI RESEARCH
QLAM: A Quantum Long-Attention Memory Approach to Long-Sequence Token Modeling
arXiv CS.LG
•
ArXi:2605.13833v1 Announce Type: new Modeling long-range dependencies in sequential data remains a central challenge in machine learning. Transformers address this challenge through attention mechanisms, but their quadratic complexity with respect to sequence length limits scalability to long contexts.